Update pipeline.py

pipeline.py

@@ -373,29 +373,29 @@ class AnimateDiffControlNetPipeline(
         return prompt_embeds, negative_prompt_embeds
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
-        def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
-            dtype = next(self.image_encoder.parameters()).dtype
+    def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
+        dtype = next(self.image_encoder.parameters()).dtype
 
-            if not isinstance(image, torch.Tensor):
-                image = self.feature_extractor(image, return_tensors="pt").pixel_values
+        if not isinstance(image, torch.Tensor):
+            image = self.feature_extractor(image, return_tensors="pt").pixel_values
     
-            image = image.to(device=device, dtype=dtype)
-            if output_hidden_states:
-                image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
-                image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
-                uncond_image_enc_hidden_states = self.image_encoder(
-                    torch.zeros_like(image), output_hidden_states=True
-                ).hidden_states[-2]
-                uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
-                    num_images_per_prompt, dim=0
-                )
-                return image_enc_hidden_states, uncond_image_enc_hidden_states
-            else:
-                image_embeds = self.image_encoder(image).image_embeds
-                image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
-                uncond_image_embeds = torch.zeros_like(image_embeds)
+        image = image.to(device=device, dtype=dtype)
+        if output_hidden_states:
+            image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
+            image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
+            uncond_image_enc_hidden_states = self.image_encoder(
+                torch.zeros_like(image), output_hidden_states=True
+            ).hidden_states[-2]
+            uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
+                num_images_per_prompt, dim=0
+            )
+            return image_enc_hidden_states, uncond_image_enc_hidden_states
+        else:
+            image_embeds = self.image_encoder(image).image_embeds
+            image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
+            uncond_image_embeds = torch.zeros_like(image_embeds)
     
-                return image_embeds, uncond_image_embeds
+            return image_embeds, uncond_image_embeds
 
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
     def prepare_ip_adapter_image_embeds(